A stock market portfolio recommender system based on association rule mining

We propose a stock market portfolio recommender system based on association rule mining (ARM) that analyzes stock data and suggests a ranked basket of stocks. The objective of this recommender system is to support stock market traders, individual investors and fund managers in their decisions by suggesting investment in a group of equity stocks when strong evidence of possible profit from these transactions is available.Our system is different compared to existing systems because it finds the correlation between stocks and recommends a portfolio. Existing techniques recommend buying or selling a single stock and do not recommend a portfolio.We have used the support confidence framework for generating association rules. The use of traditional ARM is infeasible because the number of association rules is exponential and finding relevant rules from this set is difficult. Therefore ARM techniques have been augmented with domain specific techniques like formation of thematical sectors, use of cross-sector and intra-sector rules to overcome the disadvantages of traditional ARM.We have implemented novel methods like using fuzzy logic and the concept of time lags to generate datasets from actual data of stock prices.Thorough experimentation has been performed on a variety of datasets like the BSE-30 sensitive Index, the S&P CNX Nifty or NSE-50, S&P CNX-100 and DOW-30 Industrial Average. We have compared the returns of our recommender system with the returns obtained from the top-5 mutual funds in India. The results of our system have surpassed the results from the mutual funds for all the datasets.Our approach demonstrates the application of soft computing techniques like ARM and fuzzy classification in the design of recommender systems.     

A case study on financial ratios via cross-graph quasi-bicliques

Stocks with similar financial ratio values across years have similar price movements. We investigate this hypothesis by clustering groups of stocks that exhibit homogeneous financial ratio values across years, and then study their price movements. We propose using cross-graph quasi-biclique (CGQB) subgraphs to cluster stocks, as they can define the three dimensional (3D) subspaces of financial ratios that the stocks are homogeneous in across the years, and they can also handle missing values that are rampant in the stock data. Furthermore, investors can easily analyze these 3D subspaces to explore the relations between the stocks and financial ratios. We develop a novel algorithm, CGQBminer, which mines the complete set of CGQB subgraphs from the stock data. Through experimental analysis, we show that the hypothesis is valid. Furthermore, we demonstrate that having an investment strategy which uses groups of stocks mined by CGQB subgraphs have higher returns than one that does not. We also conducted an extensive performance analysis on CGQBminer, and show that it is efficient across different 3D datasets and parameter settings.     

基于遗传编程的中国股票市场有效性检验

现代资本市场理论与金融投资实践之间存在着有效市场假说与技术分析之间的矛盾,使用流行的技术交易规则检验股票市场有效性可能导致两种结论偏差。遗传编程使用树形结构表示问题的候选解,可以很好地描述技术交易规则。利用遗传编程算法生成一种技术交易策略,并用其检验上证综合指数和5个沪深股市个股。回测结果表明,提出的方法相对于“买入-持有”策略能够获得超额收益,并且优于常用的流行技术指标,也说明我国股票市场并未达到弱式有效。     

基于模糊近似支持向量回归的股价预测研究

股价预测是投资策略形成和风险管理模型发展的基础。为了降低股价变化趋势中的噪声信息和投资者关于两种股价预测误差的不同偏好对股价预测的影响,提出了基于信噪比的模糊近似支持向量回归(FPSVR)的股价预测模型。首先构建信噪比输入变量,然后引入模糊隶属度和双边权重测量方法对支持向量回归(SVR)模型进行改进,最后借助沪深300成份股2008至2019年的股票时间序列日数据,按照股市的波动情况将其分为三个阶段(牛市、熊市、震荡市),并建立三个基准模型进行对比分析。研究结果表明:与三个基准模型相比,所提出的股价预测模型的预测误差最低;与原有的SVR模型相比,FPSVR模型可以更好地对处于牛市和震荡市阶段的股票时间序列进行股价预测。     

基于深度强化学习的股市操盘手模型研究

股票市场具有变化快、干扰因素多、周期数据不足等特点,股票交易是一种不完全信息下的博弈过程,单目标的监督学习模型很难处理这类序列化决策问题。强化学习是解决该类问题的有效途径之一。提出了基于深度强化学习的智能股市操盘手模型ISTG（Intelligent Stock Trader and Gym）,融合历史行情数据、技术指标、宏观经济指标等多数据类型,分析评判标准和优秀控制策略,加工长周期数据,实现可增量扩展不同类型数据的复盘模型,自动计算回报标签,训练智能操盘手,并提出直接利用行情数据计算单步确定性动作值的方法。采用中国股市1400多支的有10年以上数据的股票进行多种对比实验,ISTG的总体收益达到13%,优于买入持有总体−7%的表现。     

Intelligent stock trading system based on improved technical analysis and Echo State Network

Stock trading system to assist decision-making is an emerging research area and has great commercial potentials. Successful trading operations should occur near the reversal points of price trends. Traditional technical analysis, which usually appears as various trading rules, does aim to look for peaks and bottoms of trends and is widely used in stock market. Unfortunately, it is not convenient to directly apply technical analysis since it depends on person’s experience to select appropriate rules for individual share. In this paper, we enhance conventional technical analysis with Genetic Algorithms by learning trading rules from history for individual stock and then combine different rules together with Echo State Network to provide trading suggestions. Numerous experiments on S&P 500 components demonstrate that whether in bull or bear market, our system significantly outperforms buy-and-hold strategy. Especially in bear market where S&P 500 index declines a lot, our system still profits.     

An integrated portfolio optimisation procedure based on data envelopment analysis,artificial bee colony algorithm and genetic programming

Portfolio optimisation is an important issue in the field of investment/financial decision-making and has received considerable attention from both researchers and practitioners. However, besides portfolio optimisation, a complete investment procedure should also include the selection of profitable investment targets and determine the optimal timing for buying/selling the investment targets. In this study, an integrated procedure using data envelopment analysis (DEA), artificial bee colony (ABC) and genetic programming (GP) is proposed to resolve a portfolio optimisation problem. The proposed procedure is evaluated through a case study on investing in stocks in the semiconductor sub-section of the Taiwan stock market for 4 years. The potential average 6-month return on investment of 9.31% from 1 November 2007 to 31 October 2011 indicates that the proposed procedure can be considered a feasible and effective tool for making outstanding investment plans, and thus making profits in the Taiwan stock market. Moreover, it is a strategy that can help investors to make profits even when the overall stock market suffers a loss.     

基于关联规则的股票涨跌趋势的预测研究

随着证券市场的迅猛发展,股票数据信息爆炸式增长,如何从庞大的敷据中找到有用的信息来指导投资者的投资行为成为一个重要的学术研究方向.从数据挖掘技术入手,在关联规则分析的处理算法基础上,引入股票成交量数据项以及二雏时间模式对股票敷据进行数据挖掘,并对比试验的结果.试验证明,该改进时投资者提供了更为有效的决策支持.     

FuzzyTree crossover for multi-valued stock valuation

Stock valuation is very important for fundamental investors in order to select undervalued stocks so as to earn excess profits. However, it may be difficult to use stock valuation results, because different models generate different estimates for the same stock. This suggests that the value of a stock should be multi-valued rather than single-valued. We therefore develop a multi-valued stock valuation model based on fuzzy genetic programming (GP). In our fuzzy GP model the value of a stock is represented as a fuzzy expression tree whose terminal nodes are allowed to be fuzzy numbers. There is scant literature available on the crossover operator for our fuzzy trees, except for the vanilla subtree crossover. This study generalizes the subtree crossover in order to design a new crossover operator for the fuzzy trees. Since the stock value is estimated by a fuzzy expression tree which calculates to a fuzzy number, the stock value becomes multi-valued. In addition, the resulting fuzzy stock value induces a natural trading strategy which can readily be executed and evaluated. These experimental results indicate that the fuzzy tree (FuzzyTree) crossover is more effective than a subtree (SubTree) crossover in terms of expression tree complexity and run time. Secondly, shorter training periods produce a better return of investment (ROI), indicating that long-term financial statements may distort the intrinsic value of a stock. Finally, the return of a multi-valued fuzzy trading strategy is better than that of single-valued and buy-and-hold strategies.     

基于遗传算法的股票市场选择模型

为提高投资者在股票市场的收益,解决在证券投资中股票选择这一重要问题,提出一种基于遗传算法的股票选择模型。算法以上市公司的财务指标为样本特征,为克服K-means算法的不稳定性,采用基于遗传算法的K-means算法对同一板块股票进行聚类分析,剔除财务指标较差的一类中的股票。对筛选条件编码,为解决传统遗传算法处理复杂问题时存在的过早收敛现象,提出改进的遗传算子,利用改进的遗传算法寻找使股票市场投资收益最大化的选股模型参数。实验结果表明,该算法在股票选择上具有较好的效果,可供市场投资者借鉴。     

A novel stock recommendation system using Guba sentiment analysis

Investment recommendation has been one of the hottest topics in the finance area which can help investors to get more profits and to avoid loss. Existing recommendation systems mostly depend on analysis of trading data and company profit prediction. Though many works show that there is a positive correlation between investors’ sentiment and the finance market trends, few recommendation theories have been built based on sentiment. The primary reason is the difficulty to measure investors’ sentiment. In this work, a novel stock recommendation system is developed based on a proposed theory concerning the correlation between Guba-based sentiment of the retail investors and the stock market trends in China. To verify four hypotheses of the theory, a novel method is proposed to measure the investors’ sentiment by exploiting the large volumes of emotion enriched texts posted in Guba, which is online social platform for individual investors to share news and opinions concerning their favorite stocks. Results show the correctness of the proposed theory: (1) there is a positive correlation between Guba-based sentiment and the stock market trends; 2) the higher the post volumes and agreement, more proficiency the bullishness would be; and (3) a long-lasting negative Guba-based sentiment indicates the arrival of the bear market. The proposed recommendation system consists of three criteria accordingly to ensure the portfolio to meet requirements of the theory. Finally, experiments are implemented using the real data of Chinese stock market from March 2009 to March 2016 and the results show the effectiveness of the proposed system in recommending lucrative stocks and the theoretical cumulate profit is about eight times of the CSI300 in the period.     

挖掘最大频繁项集的事务集迭代算法

发现最大频繁项目集是数据挖掘应用中的关键问题;提出一种新的基于事务集迭代的求最大频繁项集算法,该算法在每次迭代时,通过对输入事务集的两次扫描,生成所有阶数的候选项集和频繁项集;每次迭代后又生成新的事务集作为下一次迭代的输入,而候选最大频繁项集集合则随着迭代不断地趋于完整。该算法不需要生成K-1阶候选项集或频繁树,有别于已有的经典算法;同时由于用于迭代的事务集的数据量会快速缩减,从而也可有效降低算法的时间复杂度。实验表明在大数据量和小最小支持度时该算法更为有利。     

基于LSTM的股票价格预测建模与分析

股价波动是一个高度复杂的非线性系统，其股票的调整不是按照均匀的时间过程推进，具有自身的推进过程。结合LSTM（Long Short-Term Memory）递归神经网络的特性和股票市场的特点，对数据进行插值、小波降噪、归一化等预处理操作后，推送到搭建的不同LSTM层数与相同层数下不同隐藏神经元个数的LSTM网络模型中进行训练与测试。对比评价指标与预测效果找到适宜的LSTM层数与隐藏神经元个数，提高了预测准确率约30%。测试结果表明，该模型计算复杂度小，预测准确率有所提高，不仅能在股票投资前对预测股票走势提供有益的参考，还能帮助投资者在对实际股价有了进一步的认知后构建合适的股票投资策略。     

股指期货套利管理系统

针对我国股指期货市场存在的问题——理论与实际应用脱节,造成股指期货市场的发展需求和实际发展条件不平衡,提出构建股指期货套利管理系统的策略. 依据实地调研资料进行系统设计,使用C#实现股指期货套利管理系统（SIFAM-System）. 系统实现了基于基差的跨期套利和基于无套利区间的跨期套利,以及与套利相关的信息管理功能. SIFAM-System利用计算机实现模型计算、行情监控、套利机会的判断及开平仓,达到了辅助投资者决策的目的,也为股指期货市场的发展问题提供了从理论到实现的解决策略.     

Intelligent agent-assisted adaptive order simulation system in the artificial stock market

Agent-based computational economics (ACE) has received increased attention and importance over recent years. Some researchers have attempted to develop an agent-based model of the stock market to investigate the behavior of investors and provide decision support for innovation of trading mechanisms. However, challenges remain regarding the design and implementation of such a model, due to the complexity of investors, financial information, policies, and so on. This paper will describe a novel architecture to model the stock market by utilizing stock agent, finance agent and investor agent. Each type of investor agent has a different investment strategy and learning method. A prototype system for supporting stock market simulation and evolution is also presented to demonstrate the practicality and feasibility of the proposed intelligent agent-based artificial stock market system architecture.     

基于Vague集相似度量的股票选择

选择具有投资价值的股票是股市投资者面临的现实问题,价值发现型的投资理念迫切需要科学的股票选择方法与之相对应.本文基于Vague集相似性度量提出一种价值型股票投资的选择方法.用Vague集核属性构建价值型股票评价体系后,使用本文提出的Vague集相似性度量方法挑选出与目标预期相符的股票.本文的方法对于投资者选择适合投资的股票具有现实意义.     

The role of algorithmic trading systems on stock market efficiency

The rapid development of information technology has changed the dynamics of financial markets. The main purpose of this study is laid on examining the role of IT based stock trading on financial market efficiency. This research specifically focused on algorithmic trading. Algorithmic trading enables investors to trade stocks through a computer program without the need for human interventions. Based on an empirical analysis of the Korean stock market, this study discovered the positive impact of algorithmic trading on stock market efficiency at three-fold. First, the study results indicate that algorithmic trading contributes to the reduction in asymmetric volatility, which causes inefficiency of information in a stock market. Second, an algorithmic trading also increases the operation efficiency of a stock market. Arbitrage trading contributes on the equilibrium between the spot market and futures market as well as on the price discovery. Third, algorithmic trading provides liquidity for market participants contributing to friction free transactions. The research results indicate that stock exchanges based on electronic communications networks (ECNs) without human intervention could augment a financial market quality by increasing trading share volumes and market efficiency so that it can eventually contribute to the welfare of market investors.     

Predicting published news effect in the Brazilian stock market

The Efficient Market Hypothesis states that the value of an asset is given by all information available in the present moment. However, there is no possibility that a single financial analyst be aware of all published news which refers to a collection of stocks in the moment they are published. Thus, a computer system that applies text mining techniques and the GARCH model for predicting the volatility of financial assets may helps analysts and simple investors classifying automatically the news which cause the higher impact on stock market behavior. This work has the goal of creating a method for analyzing Portuguese written news’s content about companies that have their stocks negotiated in a stock market and trying to predict what kind of effect these news will cause in the Brazilian stock market behavior. Also, it was demonstrated in this study that it is possible to find out whether certain news may cause a considerable impact on prices of a negotiated stock.     

Market sentiment dispersion and its effects on stock return and volatility

Behavioral economics has revealed that investor sentiment can profoundly affect individual behavior and decision-making. Recently, the question is no longer whether investor sentiment affects stock market valuation, but how to directly measure investor sentiment and quantify its effects. Before the era of big data, research uses proxies as a mediator to indirectly measure investor sentiment, which has proved elusive due to insufficient data points. In addition, most of extant sentiment analysis studies focus on institutional investors instead of individual investors. This is despite the fact that United States individual investors have been holding around 50% of the stock market in direct stock investments. In order to overcome difficulties in measuring sentiment and endorse the importance of individual investors, we examine the role of individual sentiment dispersion in stock market. In particular, we investigate whether sentiment dispersion contains information about future stock returns and realized volatility. Leveraging on development of big data and recent advances in data and text mining techniques, we capture 1,170,414 data points from Twitter and used a text mining method to extract sentiment and applied both linear regression and Support Vector Regression; found that individual sentiment dispersion contains information about stock realized volatility, and can be used to increase the prediction accuracy. We expect our results contribute to extant theories of electronic market financial behavior by directly measuring the individual sentiment dispersion; raising a new perspective to assess the impact of investor opinion on stock market; and recommending a supplementary investing approach using user-generated content.     

A Bayesian information criterion for portfolio selection

The mean-variance theory of Markowitz (1952) indicates that large investment portfolios naturally provide better risk diversification than small ones. However, due to parameter estimation errors, one may find ambiguous results in practice. Hence, it is essential to identify relevant stocks to alleviate the impact of estimation error in portfolio selection. To this end, we propose a linkage condition to link the relevant and irrelevant stock returns via their conditional regression relationship. Subsequently, we obtain a BIC selection criterion that enables us to identify relevant stocks consistently. Numerical studies indicate that BIC outperforms commonly used portfolio strategies in the literature.